Real-time, multi-modal sensor fusion for mission-critical defense and enterprise operations
A Reinforcement Learning-driven sensor fusion platform that combines electromagnetic signals, thermal imaging, and video inputs to deliver high-confidence target detection at the network edge—with no cloud dependency.
Seamlessly integrates RF/electromagnetic, thermal, and visual sensors into a unified perception system for unparalleled situational awareness.
Operates independently at the network edge with zero cloud dependency, ensuring low-latency responses critical for defense operations.
Reinforcement learning architecture that continuously adapts and improves detection accuracy across diverse operational environments.
From protecting critical infrastructure to enabling autonomous operations, FuxionAI delivers intelligence where it matters most.
Real-time detection and classification of low, slow, and small (LSS) drones threatening civilian and military installations.
Enable drones and autonomous systems to detect and intelligently navigate around electromagnetic interference zones.
Multi-layered threat detection for forward operating bases, critical infrastructure, and secure facilities.
Intelligence, surveillance, and reconnaissance missions with AI-driven contextual awareness and decision support.
Autonomous drones and robots perform complex infrastructure inspections with contextual guidance and anomaly detection.
Comprehensive security and operational monitoring for corporate campuses, data centers, and industrial facilities.
Early warning systems combining thermal and visual analysis for rapid fire detection and emergency response.
Continuous monitoring of power grids, pipelines, and transportation networks with predictive maintenance insights.
Led by experts in AI, robotics, and defense technology with deep domain expertise.
Rajesh Rasalkar is a seasoned Wireless AI/ML Architect with 25 years at Meta, Oracle, and Bell Labs, holding multiple patents in Edge Cloud Computing.
PhD in machine learning, computer vision, and signal processing, CTO
PhD in Mathematics, Univ. of Virginia (UVA) using machine learning/deep learning, computer vision, control, and meta-learning
Accomplished Semiconductor and Mobile Communications business and technology visionary
Serial entrepreneur with a Master’s in Economics, known for building and scaling high-impact technology ventures. Founder with multiple successful exits.
Latest announcements, news coverage, and educational webinars
The strategic partnership enhances ACL Digital’s capabilities with PhoenixAI.tech, focusing on AI drone solutions for UAVs, IoT, and edge computing to revolutionize operations significantly Beyond Visual Line of Sight (BVLOS) missions.
Read Full Release →PhoenixAI combines it's breakthrough Multi-Sensor Fusion technology with Atriano’s global execution network — bringing mission-ready, intelligent defense solutions to governments and enterprises worldwide.
Read Full Release →Join our CTO and Head of AI Research Vignesh Manohar as they demonstrate FuxionAI's capabilities and discuss the future of AI-Powered Drones and edge-based autonomous sensing.
Watch on YouTubePhoenixAI P-NEXUS is fully integrated, TAA/NDAA-compliant Edge Autonomy AI Stack built to accelerate next-gen drone and robotics development. P-NEXUS combines Pixhawk flight control, NVIDIA Jetson Nano edge compute, 5G modem, and global eSIM into a plug-and-play platform that removes integration complexity and unlocks resilient, software-defined autonomy.
Read Full Release →Drones are vulnerable to malicious jamming technologies that can disrupt critical operations. Learn more about PhoenixAI's patented software based algorithm technology for anti-jamming.
Watch on YouTubePhoenixAI’s 5G-based Command and Control (C2) enables low-latency, high-bandwidth, and fully encrypted connectivity for smarter, safer, and more autonomous drone missions.
Read Full Release →We’re excited to announce the successful launch of out AI/ML tech that brings together drones, stereo vision, and 3D reconstruction.
Read Full Release →Learn from security experts about implementing effective counter-drone systems, including site assessment, deployment strategies, and ROI analysis.
Watch on YouTubeDrones have found extensive use in military applications. Avoiding enroute RF jammers that can saturate the RF receiver and inhibit communication is a major challenge. Additionally, GNSS jammers can render drones completely nonfunctional by not allowing the drone to communicate with GPS satellites to ascertain their location. This work addresses these challenges by developing a reinforcement learning (RL) algorithm that is able to optimize the wireless connectivity while avoiding jammers along the drone route. Notably, the algorithm ensures minimal deviation from the planned path in the presence of jammers.
Read on IEEE Spectrum →Partner with us to bring autonomous edge intelligence to your operations.